ObjectiveTo systematically evaluate the effect of intermittent theta burst stimulation (iTBS) on lower extremity motor function and balance function in patients with stroke. MethodsA systematic literature search was conducted in the Cochrane Library, Embase, PubMed, Web of Science, CNKI, SinoMed, Wanfang data and VIP from inception to February 19, 2025. Randomized controlled trials comparing iTBS with conventional rehabilitation or sham iTBS in patients with post-stroke lower extremity motor and balance dysfunction were included. The methodological quality of the included studies was assessed using the Cochrane Risk of Bias Tool. Meta-analysis was performed using RevMan 5.4 and Stata 14.0. ResultsA total of 16 articles involving 647 patients were included. Meta-analysis showed that iTBS improved the Fugl-Meyer Assessment-Lower Extremities score (MD = 2.58, 95%CI 1.61 to 3.55, P < 0.001), Berg Balance Scale score (MD = 4.11, 95%CI 2.43 to 5.79, P < 0.001), Barthel Index score (MD = 4.95, 95%CI 0.97 to 8.92, P = 0.010) and motor-evoked potential (MEP) latency (MD = -1.42, 95%CI -2.54 to -0.30, P = 0.010). Subgroup analyses suggested that at subacute stage, cerebellar iTBS, more than ten treatment sessions, and 1 200 pulses per day may be more effective. ConclusioniTBS may improve lower extremity motor function and activities of daily living in patients with stroke, and shorten MEP latency, and it may also confer potential benefits in improving balance function in these patients.

BACKGROUND:Impaired postural control is an important risk factor for falls and secondary damage in patients with idiopathic normal pressure hydrocephalus.Most of the existing studies have analyzed the gait parameters of patients during straight-line walking,but few have analyzed the postural stability characteristics of patients during static and dynamic activities. OBJECTIVE:To analyze the characteristics of postural stability in elderly patients with idiopathic normal pressure hydrocephalus. METHODS:Twenty-two patients clinically diagnosed with idiopathic normal pressure hydrocephalus at the Department of Neurosurgery,Huadong Hospital Affiliated to Fudan University,Shanghai,China,from September 2022 to February 2023 were selected as the patient group,and 18 healthy accompanying family members were selected as the healthy control group.The postural stability characteristics of the subjects were assessed using the Timed Up-and-Go Test,Multi-Directional Reach Test,Berg Balance Scale,and Static Balance Function Test(reaction time,speed of movement,directional control,maximum offset distance,and endpoint travel). RESULTS AND CONCLUSION:The time required to complete the Timed Up-and-Go Test was significantly longer in the patient group than in the healthy control group(P＜0.05).The results of the stretching test in the four directions of anterior,posterior,leftand right were significantly lower in the patient group than in the healthy control group(P＜0.05).The Berg Balance Scale scores in the patient group were lower than those in the healthy control group(P＜0.05).In the Static Balance Function Test,the results of reaction,movement speed,directional control,maximum offset distance and endpoint travel index were smaller in the patient group than the healthy control group(P＜0.05).To conclude,patients with idiopathic normal pressure hydrocephalus exhibit overall postural control deficits,and impaired reaction and execution abilities make these patients unable to make timely and accurate motor responses in the face of disturbances from internal or external sources,resulting in postural instability and increasing the risk of falls.

Objective: To investigate the association between screen time (ST) during leisure time and anxiety-depression symptoms among middle school students, so as to provide a basis for formulating relevant intervention measures. Methods: From November to December 2023, a stratified cluster random sampling method was used to select 2 542 students from junior and senior high school in Taiyuan City. A self designed questionnaire, the Generalized Anxiety Disorder Scale (GAD-7), and the Patient Health Questionnaire (PHQ-9) were used to investigate ST and anxiety/depression symptoms among middle school students. The Logistic regression model was used to explore the association of ST with symptoms of anxiety and depression, as well as with anxiety and depression comorbiditles (CAD). Results: The detection rates of anxiety symptoms, depression symptoms, and CAD were 13.69%, 15.77%, and 10.11%, respectively. The median ST was 2.00 h/d [interquartile range ( IQR =2.38) for weekly averages], with 0.33 h/d ( IQR =1.67) on work days and 5.00 h/d ( IQR=5.50) on rest days. Logistic regression analysis indicated that the total ST of mobile phones during rest days ( OR =1.07, 1.10, 1.11) and the averages ST of mobile phones over a week ( OR = 1.20 , 1.22, 1.29), as well as the total ST of all screen types during rest days ( OR =1.04, 1.04, 1.05) and the averages ST of all screen types over a week ( OR =1.08, 1.09, 1.21) were positively correlated with anxiety symptoms, depression symptoms, and CAD (all P <0.01). Conclusions Among middle school students in Taiyuan City, screen time is positively correlated with symptoms of anxiety or depression and the comorbidity of anxiety and depression, especially smartphone screen time and weekend screen use. Therefore, measures should be implemented to reduce unnecessary screen time among middle school students, especially the use of mobile phones, in order to mitigate the occurrence of anxiety and depression.

BACKGROUND: Inflammation plays a critical role in severe cerebral venous thrombosis (CVT) pathogenesis, but the benefits of anti-inflammatory therapies remain unclear. This study aimed to investigate the association between steroid therapy combined with anticoagulation and the prognosis of acute/subacute severe CVT patients. METHODS: A prospective cohort study enrolled patients with acute/subacute severe CVT at Xuanwu Hospital (July 2020-January 2024). Patients were allocated into steroid and non-steroid groups based on the treatment they received. Functional outcomes (modified Rankin scale [mRS]) were evaluated at admission, discharge, and 6 months after discharge. Serum high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), cerebrospinal fluid (CSF) IL-6, and intracranial pressure were measured at admission and discharge in the steroid group. Fundoscopic Frisén grades were assessed at admission and 6 months after discharge. Univariate and multivariate logistic regression were used to evaluat associations between steroid use and favorable outcomes (mRS ≤2) at the 6-month follow-up. Paired tests assessed changes in hs-CRP and other variables before and after treatment, and Spearman's correlations were used to analyze relationships between these changes and functional improvements. RESULTS: A total of 107 and 58 patients in the steroid and non-steroid groups, respectively, were included in the analysis. Compared with the non-steroid group, the steroid group had a higher likelihood of achieving an mRS score of 0-2 (93.5% vs . 82.5%, odds ratio [OR] = 2.98, P  = 0.037) at the 6-month follow-up. After adjusting for confounding factors, the result remained consistent. Pulsed steroid therapy did not increase mortality during hospitalization or follow-up, nor did it lead to severe steroid-related complications (all P  >0.05). Patients in the steroid group showed a significant reduction in serum hs-CRP, IL-6, CSF IL-6, and intracranial pressure at discharge compared to at admission, as well as a significant reduction in the fundoscopic Frisén grade at the 6-month follow-up compare to at admission (all P  <0.001). A reduction in serum inflammatory marker levels during hospitalization positively correlated with improvements in functional outcomes ( P  <0.05). CONCLUSION: Short-term steroid use may be an effective and safe adjuvant therapy for acute/subacute severe CVT when used alongside standard anticoagulant treatments, which are likely due to suppression of the inflammatory response. However, these findings require further validation in randomized controlled trials. TRAIL REGISTRATION ClinicalTrials.gov , NCT05990894.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Anticoagulants/therapeutic use* ; C-Reactive Protein/metabolism* ; Interleukin-6/metabolism* ; Intracranial Thrombosis/drug therapy* ; Prospective Studies ; Steroids/therapeutic use* ; Venous Thrombosis/drug therapy*

Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,of-fering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accu-racy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summa-rizes the application of AI in drug development,particularly in drug-target prediction,and offers rec-ommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional“one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework for unraveling TCM’s multitarget and multipathway mechanisms. Recent advancements in artificial intelligence, particularly large language models (LLMs), further enhance data integration, target identification, and clinical decision-making. This review synthesizes current progress in the application of network pharmacology and LLMs in TCM, highlighting their potential to deepen mechanistic insights and optimize drug discovery. By bridging traditional medical wisdom with modern computational tools, this integrative approach aims to advance the scientific validation of TCM and foster innovative healthcare solutions.

Objective To analyze the clinical efficacy and safety of Huoxue Xiaoyi decoction combined with exercise in the treatment of ovarian endometriosis.Methods 36 patients with ovarian endometriosis treated in Guang'anmen Hospital,China Academy of Chinese Medical Sciences from May 2022 to November 2022 were selected and treated with Huoxue Xiaoyi decoction combined with exercise for 3 months.The size of ovarian ectopic cyst,dysmenorrhea VAS score,PBAC score,female hormones,serum CA125 and CA199,catecholamines and cytokines levels were observed to evaluate the clinical efficacy and safety.Results After treatment,the maximum diameter of ovarian ectopic cyst,dysmenorrhea VAS score,PBAC score,cytokines(serum IL-1β,IL-2,IL-4,IL-6,IL-8,IL-10)levels were significantly lower than those before treatment(P<0.05).The levels of SHBG and adrenaline were significantly higher than those before treatment(P<0.05).During the treatment,the patient had no adverse drug reactions,vital signs,liver,and kidney function indexes were normal.Conclusion Huoxue Xiaoyi decoction combined with exercise therapy can reduce the size of ovarian ectopic cyst,relieve dysmenorrhea,regulate menstrual volume,increase the level of adrenaline,increase the level of sex hormone binding globulin,reduce the level of cytokines,and has fewer adverse reactions and good safety.

In recent years, the ongoing development of transcranial electrical stimulation (TES) and transcranial magnetic stimulation (TMS) has demonstrated significant potential in the treatment and rehabilitation of various brain diseases. In particular, the combined application of TES and TMS has shown considerable clinical value due to their potential synergistic effects. This paper first systematically reviews the mechanisms underlying TES and TMS, highlighting their respective advantages and limitations. Subsequently, the potential mechanisms of transcranial electromagnetic combined stimulation are explored, with a particular focus on three combined stimulation protocols: Repetitive TMS (rTMS) with transcranial direct current stimulation (tDCS), rTMS with transcranial alternating current stimulation (tACS), and theta burst TMS (TBS) with tACS, as well as their clinical applications in brain diseases. Finally, the paper analyzes the key challenges in transcranial electromagnetic combined stimulation research and outlines its future development directions. The aim of this paper is to provide a reference for the optimization and application of transcranial electromagnetic combined stimulation schemes in the treatment and rehabilitation of brain diseases.
Humans ; Transcranial Magnetic Stimulation/methods* ; Transcranial Direct Current Stimulation/methods* ; Brain Diseases/therapy*

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.